Network Engineers with AI/ML exposure in US.
5+ years of hands-on experience
Network SMEs will collaborate with data science and artificial intelligence researchers to hand-write diagnostic steps, solutions, and expert recommendations for diagnosing and resolving complex network problems. SMEs will leverage their deep knowledge of enterprise network troubleshooting, familiarity with industry-leading tools (e.g., Cisco Meraki, ThousandEyes, Splunk), and expertise in using command line interface (CLI) diagnostics to ensure that model outputs are accurate, actionable, and relevant. SMEs will contribute authoritative training and evaluation sets, document recommended actions and provide impact statements to further inform model development.
Required Skills:
- Expert-level knowledge of enterprise networking: Deep understanding of Cisco routing and switching products, with a focus on large-scale campus and enterprise networks.
- Extensive troubleshooting experience: Demonstrated experience diagnosing and resolving complex network issues, including root cause analysis and multi-vendor environments.
- Proficiency with CLI diagnostics: Expert knowledge of Cisco CLI, including the use of show commands and other diagnostics for problem investigation.
- Experience with network monitoring and analytics platforms: Hands-on experience with tools such as Cisco Meraki, ThousandEyes, and Splunk for monitoring, troubleshooting, and performance analysis.
- Experience documenting technical solutions: Strong ability to create detailed methods of action, recommendations, and impact statements for network problems and proposed solutions.
- Familiarity with automation and scripting: Working knowledge of Python and automation frameworks (e.g., Ansible, Terraform) for interacting with APIs and automating network tasks.
- Experience with software defect analysis: Familiarity with identifying software-related network issues and evaluating the impact of software bugs and vulnerabilities.
- Experience with training/evaluation data creation: Ability to design, curate, and annotate realistic problem scenarios and training examples for use in ML and GenAI model development.
Deep domain expertise in enterprise networking and troubleshooting is required to properly assess the accuracy and effectiveness of model-generated recommendations and diagnostic outputs. SMEs will provide authoritative evaluation and gold-standard examples to train and validate models, ensuring relevance to real-world network operations and support scenarios.
Services and Deliverables
- Review and evaluation of problem statements: Evaluators will review network problem descriptions and associated diagnostic data, validating the relevance and sufficiency of information for identifying root causes and remediations.
- Validation of model outputs: Assess the accuracy and practicality of AI-generated diagnostic steps, recommendations, and remediation actions. Provide clear, concise summaries when disagreeing with model outputs, including nuanced reasoning and expert insights.
- Creation of training and evaluation sets: Develop realistic network problem scenarios, recommended actions, and impact statements. Contribute expert-crafted examples for use in model training and evaluation.
- Documentation and reporting: Create comprehensive records of assessments, recommendations, and findings. Maintain quality control over annotated datasets and ensure consistency with project standards.
- Quality assurance and iterative feedback: Review and refine labeled datasets and model outputs for accuracy and consistency. Provide feedback to improve annotation guidelines, validation processes, and supporting tools.
- Collaboration: Work closely with data scientists, engineers, and project managers to align SME contributions with overall project objectives and deliverables.
- Evaluate AI-generated troubleshooting steps for a simulated network outage in a campus network, using diagnostic data from Meraki and Thousand Eyes.
- Author a set of annotated training examples depicting common security misconfigurations in enterprise switch environments, including proposed remediations and potential impacts.
- Review model-generated recommendations for addressing high latency observed in network devices, and provide feedback on their technical accuracy and completeness.
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